Article

High-priority drug-drug interactions for use in electronic health records

Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 04/2012; 19(5):735-43. DOI: 10.1136/amiajnl-2011-000612
Source: PubMed

ABSTRACT

To develop a set of high-severity, clinically significant drug-drug interactions (DDIs) for use in electronic health records (EHRs).
A panel of experts was convened with the goal of identifying critical DDIs that should be used for generating medication-related decision support alerts in all EHRs. Panelists included medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Candidate DDIs were assessed by the panel based on the consequence of the interaction, severity levels assigned to them across various medication knowledge bases, availability of therapeutic alternatives, monitoring/management options, predisposing factors, and the probability of the interaction based on the strength of evidence available in the literature.
Of 31 DDIs considered to be high risk, the panel approved a final list of 15 interactions. Panelists agreed that this list represented drugs that are contraindicated for concurrent use, though it does not necessarily represent a complete list of all such interacting drug pairs. For other drug interactions, severity may depend on additional factors, such as patient conditions or timing of co-administration.
The panel provided recommendations on the creation, maintenance, and implementation of a central repository of high severity interactions.
A set of highly clinically significant drug-drug interactions was identified, for which warnings should be generated in all EHRs. The panel highlighted the complexity of issues surrounding development and implementation of such a list.

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Available from: Blackford Middleton, Aug 23, 2015
    • "It is likely that each of these factors played a role. Several studies have shown that customization of the severity classification can moderately improve alert acceptance , although other studies found no improvement [3,6,10,11,13,43,44]. We customized the severity classification of DDIs not only at the class level, but also for specific combinations within class–class interactions. "
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    ABSTRACT: Clinical decision support (CDS) systems are frequently used to reduce unwanted drug-drug interactions (DDIs) but often result in alert fatigue. The main objective of this study was to investigate whether a newly developed context-specific DDI alerting system would improve alert acceptance. A controlled pre-post intervention study was conducted in 4 departments in a university hospital. After a 7-month pre-intervention period, the new system was activated in the intervention departments, while the old system remained activated in the control departments. Post-intervention data was collected for a 7-month period. A significant increase of the overall acceptance rate was observed between the pre- and post-intervention period (2.2% versus 52.4%; p<0.001) for the intervention departments and between the intervention and control departments (2.5% versus 52.4%; p<0.001) in the post-intervention period. There were no significant differences in acceptance rates between the pre- and post-intervention period in the control departments and also not between the control and intervention departments in the pre-intervention period. The improvement was probably related to several optimization strategies including the customization of the severity classification, the creation of individual screening intervals, the inclusion of context factors for risk assessment, the new alert design and the creation of a follow-up system. The marked increase in alert acceptance looks promising and should be further evaluated after hospital wide implementation. System aspects that require further optimization were identified and will be developed. Further research is warranted to develop context-aware algorithms for complex class-class interactions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    No preview · Article · May 2015 · International Journal of Medical Informatics
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    • "In order to illustrate how our approach supports the alignment of drug classes between MeSH and ATC, we applied our framework to a set of clinically relevant drug classes. We used the set of high-severity, clinically significant drug–drug interactions created by [17], in which most drugs are categorized in reference to drug classes. "
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    ABSTRACT: Background The objective of this study is to develop a framework for assessing the consistency of drug classes across sources, such as MeSH and ATC. Our framework integrates and contrasts lexical and instance-based ontology alignment techniques. Moreover, we propose metrics for assessing not only equivalence relations, but also inclusion relations among drug classes. Results We identified 226 equivalence relations between MeSH and ATC classes through the lexical alignment, and 223 through the instance-based alignment, with limited overlap between the two (36). We also identified 6,257 inclusion relations. Discrepancies between lexical and instance-based alignments are illustrated and discussed. Conclusions Our work is the first attempt to align drug classes with sophisticated instance-based techniques, while also distinguishing between equivalence and inclusion relations. Additionally, it is the first application of aligning drug classes in ATC and MeSH. By providing a detailed account of similarities and differences between drug classes across sources, our framework has the prospect of effectively supporting the creation of a mapping of drug classes between ATC and MeSH by domain experts.
    Full-text · Article · Jul 2014 · Journal of Biomedical Semantics
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    • "For example, groups of clinicians have been asked to agree on which alerts could be turned off safely within a hospital system [32] and to assess the value of alerts for 120 drug–drug interactions [55]. This method has also been used to identify and refine high-severity drug–drug interactions [56] and to identify low-priority drug–drug interactions that do not require interruptive alerts [57]. These studies may provide information on some research gaps, such as determining which outcome is most relevant to the specific research question. "
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    ABSTRACT: Clinical decision support (CDS) for electronic prescribing systems (computerized physician order entry) should help prescribers in the safe and rational use of medicines. However, the best ways to alert users to unsafe or irrational prescribing are uncertain. Specifically, CDS systems may generate too many alerts, producing unwelcome distractions for prescribers, or too few alerts running the risk of overlooking possible harms. Obtaining the right balance of alerting to adequately improve patient safety should be a priority. A workshop funded through the European Regional Development Fund was convened by the University Hospitals Birmingham NHS Foundation Trust to assess current knowledge on alerts in CDS and to reach a consensus on a future research agenda on this topic. Leading European researchers in CDS and alerts in electronic prescribing systems were invited to the workshop. We identified important knowledge gaps and suggest research priorities including (1) the need to determine the optimal sensitivity and specificity of alerts; (2) whether adaptation to the environment or characteristics of the user may improve alerts; and (3) whether modifying the timing and number of alerts will lead to improvements. We have also discussed the challenges and benefits of using naturalistic or experimental studies in the evaluation of alerts and suggested appropriate outcome measures. We have identified critical problems in CDS, which should help to guide priorities in research to evaluate alerts. It is hoped that this will spark the next generation of novel research from which practical steps can be taken to implement changes to CDS systems that will ultimately reduce alert fatigue and improve the design of future systems.
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